Models for consumption

Table of Contents

This notebook gives a detailed explanation of the code contained in input-Consumption.py.

In this notebook, you will manipulate temperature time series, together with models of the thermal sensitivity of consumption.

In the end you will learn how to:

TODO : add something about sectorial decomposition of consumption

1. Thermal sensitivity of consumption

1.1. Definition

As an introduction, french speakers can read my post (contribution for translation of this site's posts are welcome) on the subject.

You can observe how consumption depends upon temperature for year 2012. Below a threshold temperature, here around 15 degres, the relation between consumption and temperature is linear. The coefficient of the linear regression is, by definition, what we call the thermal sensitivity.

1.2. Functions to estimate thermosensitivity and decompose consumption

Formally, the preceding linear regression is given by the following formula:

$$ C_t = C^{NT}_t+\rho_{h(t)}(T_t-T_0)_+$$

where $T_0$ is the threshold temperature, $(x)_+$ is $x$ for any non negative $x$ and zero otherwise, $\rho_{h(t)}$ is the thermal sensitivity for hour $h$ (if it depends upon the hour of the day) $C^{NT}_t$ is the part of the consumption that is not sensitive to the temperature. It is obtained as the residuals of the linear regression for temperatures lower than $T_0$ and is the consumption itself otherwise.

In module functions.functions_decompose_thermosensibilite, we have implemented a function to estimate the different elements in the preceding equation, given consumption, temperature and other parameters (such as $T_0$). Check the file functions_decompose_thermosensibilite.py to understand how this works.

1.3. Functions to play with thermosensitivity of consumption and resimulate

You can do other interesting things. For example, you can redecompose the electric consumption of the year X to thermosensitive and no-thermosensitive parts from the thermosensitivity of the year X and the temperatures of the year Y. It's very useful to compare the years.

In addition, with the same function Recompose to that you can also redecompose the electric consumption of the year X to thermosensitive and no-thermosensitive parts from a new table of thermosensitivity.

Now, you know everything about thermosensitivity and you can use theses functions whenever you want in the TP to cast light on interesting values and draw smart conclusions :)

2 consumption from european countries

3. Electric Vehicle consumption

4. Sector by sector temporal model of consumption

TODO : if you want to contribute, you can add models in functions.f_consumptionModels.py and explanations here and in input-Consumption.py